Exclude doublets and poor-quality cells (level 2): GCBC
3 junio 2021
1 Introduction
The objective of this notebook is to exclude the clusters of doublets or poor-quality cells.
2 Pre-processing
2.1 Load packages
library(Seurat)
library(tidyverse)2.2 Parameters
# Paths
path_to_doublets <- here::here("scRNA-seq/3-clustering/2-level_2/tmp/doublets_multiome_df_all.rds")
path_to_dir <- here::here("scATAC-seq/results/R_objects/level_2/")
path_to_obj <- str_c(
path_to_dir,
params$cell_type,
"/",
params$cell_type,
"_integrated_level_2.rds",
sep = ""
)
path_to_save <- str_c(
path_to_dir,
params$cell_type,
"/",
params$cell_type,
"_clustered_filtered_level_2.rds",
sep = ""
)
# Functions
source(here::here("scRNA-seq/bin/utils.R"))
# Colors
color_palette <- c("black", "gray", "red", "yellow", "violet", "green4",
"blue", "chocolate1", "coral2", "blueviolet",
"brown1", "darkmagenta", "deepskyblue1", "dimgray",
"deeppink1", "green", "lightgray", "hotpink1",
"indianred4", "khaki", "mediumorchid2")
# Resolutions
resolutions <- c(
NBC_MBC = 0.025,
GCBC = 0.05,
CD4_T = 0.025,
Cytotoxic = 0.01,
PC = 0.01
)
resolution <- resolutions[params$cell_type]
names(resolution) <- NULL
# Clusters to exclude
cluster_to_exclude_all <- list(
NBC_MBC = c("1"),
GCBC = c("1"),
CD4_T = c(as.character(seq(1,3))),
Cytotoxic = c(as.character(seq(1,3))),
PC = c(as.character(seq(1,8)))
)
cluster_to_exclude <- cluster_to_exclude_all[[params$cell_type]]2.3 Load data
# Seurat object
seurat <- readRDS(path_to_obj)
seurat## An object of class Seurat
## 270784 features across 27497 samples within 1 assay
## Active assay: peaks_macs (270784 features, 267464 variable features)
## 3 dimensional reductions calculated: umap, lsi, harmony3 Cluster
Here, we cluster with the resolution that allows us to separate our clusters of interest, which was decided by inspecting the results of previous notebooks.
seurat <- FindClusters(seurat, resolution = resolution)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 27497
## Number of edges: 810353
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9560
## Number of communities: 5
## Elapsed time: 4 secondsp <- DimPlot(seurat, cols = color_palette,label = T)
p
4 Exclude doublets and poor-quality cells
4.1 Exclude doublets directly from scRNAseq data using the Multiome approach
4.1.1 Visualize before filtering
multiome_doublets <- readRDS(path_to_doublets)
doublets_cells <- colnames(seurat)[which(colnames(seurat) %in% multiome_doublets$barcode)]
length(doublets_cells)## [1] 1571DimPlot(
seurat, reduction = "umap",
cols.highlight = "darkred", cols= "grey",
cells.highlight= doublets_cells,
pt.size = 0.1
)
4.1.2 Visualize after filtering
selected_cells_doublets <- colnames(seurat)[which(!colnames(seurat) %in% doublets_cells)]
seurat <- subset(seurat, cells = selected_cells_doublets)
seurat## An object of class Seurat
## 270784 features across 25926 samples within 1 assay
## Active assay: peaks_macs (270784 features, 267464 variable features)
## 3 dimensional reductions calculated: umap, lsi, harmonyDimPlot(
seurat, reduction = "umap",
pt.size = 0.1
)
4.2 Exclude problematic clusters
print("Clusters to exclude")## [1] "Clusters to exclude"cluster_to_exclude## [1] "1"selected_cells <- colnames(seurat)[!(seurat$seurat_clusters %in% cluster_to_exclude)]
length(selected_cells)## [1] 23893seurat <- subset(seurat, cells = selected_cells)
seurat## An object of class Seurat
## 270784 features across 23893 samples within 1 assay
## Active assay: peaks_macs (270784 features, 267464 variable features)
## 3 dimensional reductions calculated: umap, lsi, harmony4.2.1 Visualize after filtering
p2 <- DimPlot(seurat, cols = color_palette, label = T)
p2
5 Save
saveRDS(seurat, path_to_save)6 Session Information
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS/LAPACK: /Users/pauli/opt/anaconda3/envs/Tonsil_atlas/lib/libopenblasp-r0.3.10.dylib
##
## locale:
## [1] es_ES.UTF-8/UTF-8/es_ES.UTF-8/C/es_ES.UTF-8/es_ES.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] Signac_1.1.0.9000 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0 Seurat_3.9.9.9010 BiocStyle_2.16.1
##
## loaded via a namespace (and not attached):
## [1] rappdirs_0.3.1 SnowballC_0.7.0 rtracklayer_1.48.0 GGally_2.0.0 bit64_4.0.5 knitr_1.30 irlba_2.3.3 DelayedArray_0.14.0 data.table_1.13.2 rpart_4.1-15 RCurl_1.98-1.2 AnnotationFilter_1.12.0 generics_0.1.0 BiocGenerics_0.34.0 GenomicFeatures_1.40.1 cowplot_1.1.0 RSQLite_2.2.1 RANN_2.6.1 future_1.20.1 bit_4.0.4 spatstat.data_2.1-0 xml2_1.3.2 lubridate_1.7.9 httpuv_1.5.4 SummarizedExperiment_1.18.1 assertthat_0.2.1 xfun_0.18 hms_0.5.3 evaluate_0.14 promises_1.1.1 fansi_0.4.1 progress_1.2.2 dbplyr_1.4.4 readxl_1.3.1 igraph_1.2.6 DBI_1.1.0 htmlwidgets_1.5.2 reshape_0.8.8 stats4_4.0.3 ellipsis_0.3.1 backports_1.2.0 bookdown_0.21
## [43] biomaRt_2.44.4 deldir_0.2-3 vctrs_0.3.4 Biobase_2.48.0 here_1.0.1 ensembldb_2.12.1 ROCR_1.0-11 abind_1.4-5 withr_2.3.0 ggforce_0.3.2 BSgenome_1.56.0 checkmate_2.0.0 sctransform_0.3.1 GenomicAlignments_1.24.0 prettyunits_1.1.1 goftest_1.2-2 cluster_2.1.0 lazyeval_0.2.2 crayon_1.3.4 pkgconfig_2.0.3 labeling_0.4.2 tweenr_1.0.1 GenomeInfoDb_1.24.0 nlme_3.1-150 ProtGenerics_1.20.0 nnet_7.3-14 rlang_0.4.11 globals_0.13.1 lifecycle_0.2.0 miniUI_0.1.1.1 BiocFileCache_1.12.1 modelr_0.1.8 rsvd_1.0.3 dichromat_2.0-0 cellranger_1.1.0 rprojroot_2.0.2 polyclip_1.10-0 matrixStats_0.57.0 lmtest_0.9-38 graph_1.66.0 Matrix_1.3-2 ggseqlogo_0.1
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## [127] tools_4.0.3 future.apply_1.6.0 parallel_4.0.3 rstudioapi_0.12 foreign_0.8-80 lsa_0.73.2 gridExtra_2.3 farver_2.0.3 Rtsne_0.15 digest_0.6.27 BiocManager_1.30.10 shiny_1.5.0 Rcpp_1.0.5 GenomicRanges_1.40.0 broom_0.7.2 later_1.1.0.1 RcppAnnoy_0.0.16 OrganismDbi_1.30.0 httr_1.4.2 AnnotationDbi_1.50.3 ggbio_1.36.0 biovizBase_1.36.0 colorspace_2.0-0 rvest_0.3.6 XML_3.99-0.3 fs_1.5.0 tensor_1.5 reticulate_1.18 IRanges_2.22.1 splines_4.0.3 uwot_0.1.8.9001 RBGL_1.64.0 RcppRoll_0.3.0 spatstat.utils_2.1-0 plotly_4.9.2.1 xtable_1.8-4 jsonlite_1.7.1 spatstat_1.64-1 R6_2.5.0 Hmisc_4.4-1 pillar_1.4.6 htmltools_0.5.1.1
## [169] mime_0.9 glue_1.4.2 fastmap_1.0.1 BiocParallel_1.22.0 codetools_0.2-17 lattice_0.20-41 curl_4.3 leiden_0.3.5 openssl_1.4.3 survival_3.2-7 rmarkdown_2.5 munsell_0.5.0 GenomeInfoDbData_1.2.3 haven_2.3.1 reshape2_1.4.4 gtable_0.3.0